Bandingkan metode

	Model Struktural Marginal (MSM)	Bobot Probabilitas Invers (IPW / IPTW)
Bidang	Inferensi Kausal	Inferensi Kausal
Keluarga	Regression model	Regression model
Tahun asal	2000	2000
Pencetus	James M. Robins, Miguel A. Hernan, Babette Brumback	Robins, Hernán & Brumback
Tipe	Causal model / semiparametric weighting	Causal inference weighting estimator
Sumber perintis	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Alias	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Terkait	5	5
Ringkasan	A marginal structural model is a causal modeling framework designed to estimate the effect of a time-varying treatment in the presence of time-varying confounders that are themselves affected by prior treatment. By reweighting observations with inverse probability of treatment weights, MSMs create a pseudo-population in which confounding is eliminated, enabling unbiased estimation of causal treatment contrasts even when standard regression adjustments would fail.	Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias.